Feature Line Extraction from Point Clouds Based on Geometric Structure of Point Space

In order to improve the accuracy and rapidity of feature line extraction from point clouds, the work proposed a feature line extraction method based on geometric structure of point space. Firstly, a spatial grid dynamic division method is designed to locate the feature region of the model. A new feature points detection operator based on the linear intercept ratio is proposed according to the geometric information of points. Then, the feature points are refined by the Laplacian operator. Finally, the refined feature points are connected into the characteristic curve by the improved method of polyline growth. Compared with the feature points detection method based on surface variation (MSSV) or the angle of normal vector (SM-PD), the proposed method has low rate of error recognition with the increased noise intensity. Meanwhile, the computation time is 224.42 ms for the standard Armadillo model, less than 530.23 ms of the MSSV and 350.75 ms of the SM-PD. The experimental results show that the proposed method can accurately extract the feature points, with good noise immunity, especially suitable for the massive point cloud model.